Textile traceability method and system based on block chain

Abstract

The invention belongs to the technical field of textile traceability, and particularly relates to a textile traceability method and system based on a block chain, and the method comprises the steps: analyzing the textile data integrity based on a block chain network, judging whether the textile data is complete or not, analyzing the textile traceability access confidence, and judging whether the textile traceability access behavior is credible or not. And analyzing the textile traceability query result, comparing to obtain textile traceability data, analyzing the textile traceability data state, and comparing to obtain a textile traceability data risk point. According to the method, the problems of inaccurate and missing data possibly existing in traditional textile data traceability related information and incomplete judgment of the textile data traceability access verification state are solved, the whole production resume of a product can be restored more accurately, risk points in a supply chain can be found and positioned favorably, and the quality of the product is improved. And a basis is provided for improvement.

Description

Technical Field

[0001] The present invention belongs to the technical field of textile traceability, and specifically relates to a textile traceability method and system based on blockchain. Background Art

[0002] Textile traceability refers to tracking the entire process of textiles from raw material collection, production, processing to final sales. The purpose of textile traceability is to ensure the quality, safety and compliance of products, while improving the transparency and efficiency of the supply chain. The textile industry usually involves a relatively complex supply chain, making it difficult to track the entire life cycle of the product. Blockchain technology provides a data recording method that cannot be tampered with and is easy to verify. It can safely record and share information. Through blockchain, the production and distribution steps of textiles can be transparently recorded, thereby achieving true data traceability, which not only increases the transparency of the supply chain, but also improves the efficiency, safety and traceability of operations.

[0003] However, in the process of combining blockchain technology with textile traceability, there are still some problems. For example, traceability-related information may be inaccurate or missing, and the verification status judgment process for textile data traceability access is not comprehensive enough. This may lead to the inability to fully and accurately grasp the entire production history of the product during the traceability process, and the inability to timely and accurately identify the authenticity, completeness and credibility of the data, affecting the reliability of the traceability results. Summary of the invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a blockchain-based textile traceability method and system, which can effectively record and verify the integrity of textile data and enhance consumers' trust in the traceability results.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: Based on the blockchain network, the integrity of textile data is analyzed to determine whether the textile data is complete; Based on the complete textile data, the confidence of textile traceability access is analyzed to determine whether the textile traceability access behavior is credible; Based on the credible textile traceability access behavior, the textile traceability query results are analyzed and compared to obtain the textile traceability data; Based on the textile traceability data obtained by comparison, the textile traceability data status is analyzed to obtain the traceability data status evaluation value; Based on the traceability data status assessment value, the risk points of textile traceability data are obtained by comparison.

[0006] Preferably, the process of analyzing the integrity of the textile data and determining whether the textile data is complete is: Acquire a textile data integrity data set, and obtain a data integrity assessment value based on a comprehensive analysis of the acquired textile data integrity data set, wherein the data integrity assessment value serves as an analysis basis for determining whether the textile data is complete; comparing the data integrity assessment value with a data integrity delimitation assessment value stored in a database; If the data integrity assessment value is equal to the data integrity delimitation assessment value, the textile data corresponding to the data integrity assessment value is complete; If the data integrity assessment value is not equal to the data integrity definition assessment value, the textile data corresponding to the data integrity assessment value is incomplete, and a prompt is issued for the incomplete textile data.

[0007] Preferably, the textile data integrity data set includes the number of textile data transaction records, the number of actual textile data transactions, and the number of textile data transaction record verifications.

[0008] Preferably, the process of analyzing the confidence of the textile traceability access and judging whether the textile traceability access behavior is credible is: Obtain a textile traceability access confidence data set, which includes the total number of textile traceability single access verifications, the total duration of textile traceability single access verifications, and the highest percentage of overlap between the IP address of the device used for textile traceability access and the IP address bytes of each device stored in the database; Based on the obtained textile traceability access confidence data set, a comprehensive analysis is conducted to obtain the traceability access confidence value, which is used as the analysis basis for judging whether the textile traceability access behavior is credible. Comparing the traceability access confidence value with the traceability access bounded confidence value stored in the database; If the traceability access confidence value is higher than or equal to the traceability access definition confidence value, the textile traceability access behavior corresponding to the traceability access confidence value is credible; If the traceability access confidence value is lower than the traceability access definition confidence value, the textile traceability access behavior corresponding to the traceability access confidence value is untrustworthy, and a prompt is issued for the untrustworthy textile traceability access behavior.

[0009] Preferably, the traceability access confidence value is obtained in the following manner: ; In the formula, is the confidence value of traceability access, dn is the total number of single access verifications for textile traceability, dt is the total duration of single access verification for textile traceability, cb is the maximum proportion of bytes of overlap between the IP address of the device used for textile traceability access and the IP addresses of each device stored in the database, is the compensation factor of the set dn, is the compensation factor of the set dt, is the compensation factor of the set cb, and e is a natural constant.

[0010] Preferably, the process of analyzing the textile traceability query results and comparing to obtain the textile traceability data is: Obtain a textile traceability query result data set, the textile traceability query result data set including the total number of textile traceability query result bytes, the total number of textile traceability query result fields, and the update frequency of textile traceability query records; Based on the obtained textile traceability query result data set, a comprehensive analysis is performed to obtain the traceability query result characteristic value, and the traceability query result characteristic value is used as the analysis basis for comparing the textile traceability data; The traceability query result characteristic value is compared with the textile traceability data corresponding to each traceability query result characteristic value stored in the database to obtain the textile traceability data corresponding to the traceability query result characteristic value.

[0011] Preferably, the method for obtaining the characteristic value of the traceability query result is: ; In the formula, is the characteristic value of the traceability query result, zj is the total number of bytes of the textile traceability query result, zd is the total number of fields of the textile traceability query result, pl is the update frequency of the textile traceability query record, is the compensation factor of the set zj, is the compensation factor of the set zd, is the compensation factor of the set pl.

[0012] Preferably, the process of analyzing the textile traceability data status to obtain the traceability data status evaluation value is: Obtain a textile traceability data status data set, which includes the proportion of missing product information in textile traceability data, the proportion of missing production information in textile traceability data, the proportion of missing supply chain information in textile traceability data, and the absolute value of the difference between the update frequency of textile traceability data and the reference update frequency; Based on the obtained textile traceability data status data set, a comprehensive analysis is performed to obtain the traceability data status assessment value, which is used as the analysis basis for comparing the risk points of textile traceability data; The method for obtaining the traceability data status evaluation value is as follows: ; In the formula, is the traceability data status assessment value, cq is the proportion of missing product information in textile traceability data, sq is the proportion of missing production information in textile traceability data, gl is the proportion of missing supply chain information in textile traceability data, gx is the absolute value of the difference between the update frequency of textile traceability data and the reference update frequency, The update frequency of textile traceability data, Reference update frequency for textile traceability data, is the compensation factor of the set cq, is the compensation factor of the set sq, is the compensation factor of the set gl, is the compensation factor of the set gx.

[0013] Preferably, the process of obtaining the risk points of textile traceability data by comparison based on the traceability data status evaluation value is: The traceability data status evaluation value is compared with the textile traceability data risk points corresponding to each traceability data status evaluation value stored in the database to obtain the textile traceability data risk points corresponding to the traceability data status evaluation value.

[0014] The blockchain-based textile traceability system is used to implement the above-mentioned blockchain-based textile traceability method, including a textile data integrity judgment module, an access behavior confidence analysis module, a textile traceability data comparison module, a traceability data status assessment value acquisition module and a textile traceability data risk point comparison module, wherein: The textile data integrity judgment module is used to analyze the integrity of textile data based on the blockchain network and determine whether the textile data is complete; The access behavior confidence analysis module is used to analyze the textile traceability access confidence based on the complete textile data to determine whether the textile traceability access behavior is credible; The textile traceability data comparison module is used to analyze the textile traceability query results based on the credible textile traceability access behavior and compare the textile traceability data; A traceability data status evaluation value acquisition module is used to analyze the textile traceability data status based on the textile traceability data obtained by comparison to obtain the traceability data status evaluation value; The textile traceability data risk point comparison module is used to obtain the textile traceability data risk points based on the traceability data status assessment value.

[0015] The present invention has the following beneficial effects: The present invention can effectively record and verify the integrity of textile data through the distributed ledger characteristics of blockchain, enhance consumers' trust in traceability results, conduct credibility analysis on traceability access behavior, help identify and troubleshoot malicious access behavior, and improve the security and stability of the traceability system. By analyzing and comparing complete and reliable traceability data, the entire production history of the product can be restored more accurately, which is conducive to discovering and locating risk points in the supply chain and providing a basis for improvement.

[0016] The present invention analyzes the integrity of textile data to determine whether the textile data is complete, which can effectively prove the quality and source of the product, discover potential risks in time, and avoid quality problems caused by missing information. Only complete data can provide sufficient information support for decision-making. Analyzing data integrity can help management make more accurate decisions and optimize supply chain management. By ensuring data integrity, enterprises can improve operational efficiency and reduce resource waste and time delays caused by data errors or omissions.

[0017] The present invention obtains textile traceability data risk points by comparison based on traceability data status evaluation values, and evaluates the traceability data status, so as to timely discover abnormal situations in the data, help enterprises identify potential risks early, and prevent the expansion of problems. The evaluation value provides quantitative data support, so that decision makers can make more accurate decisions based on specific risk points, optimize supply chain management and product quality control, and timely identification and processing of risk points can help enterprises improve production and supply processes in a targeted manner; based on regular evaluation and risk point identification, enterprises can continuously optimize their production processes and quality management systems, forming a virtuous continuous improvement cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the method steps of the present invention; Figure 2 It is a schematic diagram of system module connection of the present invention; Figure 3 Accessing confidence values ​​for traceability As the textile traceability access uses the device IP address and the bytes of each device IP address stored in the database, the image with the highest proportion of cb changes; Figure 4 is the characteristic value of the traceability query result Image showing the change in the total number of bytes zj of textile traceability query results. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the accompanying drawings.

[0020] Example 1: Figure 1As shown, the blockchain-based textile traceability method includes: based on the blockchain network, analyzing the integrity of textile data to determine whether the textile data is complete.

[0021] The specific analysis process is as follows: obtain a textile data integrity data set, and based on the obtained textile data integrity data set, comprehensively analyze to obtain a data integrity assessment value, which is used as the analysis basis for judging whether the textile data is complete; compare the data integrity assessment value with the data integrity delimitation assessment value stored in the database; if the data integrity assessment value is equal to the data integrity delimitation assessment value, the textile data corresponding to the data integrity assessment value is complete; if the data integrity assessment value is not equal to the data integrity delimitation assessment value, the textile data corresponding to the data integrity assessment value is incomplete, and a prompt is issued for incomplete textile data. The data integrity delimitation assessment value is 2.

[0022] Timely issuance of reminders of incomplete data can enable relevant personnel to quickly identify and correct data errors and avoid further problems caused by incomplete data; the reminder mechanism encourages data maintenance personnel to pay attention to the accuracy and completeness of the data, thereby continuously improving data quality and ensuring the reliability of subsequent analysis and decision-making; providing reminders on data integrity can increase the transparency of the data processing process, help all parties understand the authenticity and availability of the data, and thus enhance trust; by issuing reminders, data processing personnel can be urged to handle missing or erroneous data in a timely manner, improve the efficiency of the entire operation process, and reduce subsequent additional workload.

[0023] The textile data integrity data set includes the number of textile data transaction records, the number of actual textile data transactions, and the number of textile data transaction record verifications. The number of textile data transaction records represents the number of all transaction-related records in the system or platform. The records include not only successful transactions, but also failed or cancelled transactions, which are obtained based on the blockchain network; the number of actual textile data transactions refers to the actual number of transactions, which usually means that the transaction has been confirmed, including successful transactions, as well as failed or cancelled transactions. It is a specific accounting of the number of transaction records, reflecting real business activities, and is obtained based on the transaction management system. The number of actual textile data transactions reflects the transaction status of textiles in the entire supply chain, from raw material procurement to production, processing, sales and other links. By recording all transactions (including failed and cancelled transactions), the transaction information of each link can be fully displayed to ensure that the transaction history of the product is traceable. There may be multiple transaction records in each link of product production and sales. Statistics on these transaction records can help discover whether there are data omissions, supply chain management problems, etc., and take timely measures to ensure product quality; the number of textile data transaction record verifications refers to the number of times the transaction records are verified, which is obtained based on the data verification and audit system. The number of verifications reflects the degree of verification of each transaction record. High-frequency transaction record verification means that the system conducts strict checks on each transaction to ensure that there are no omissions or errors in transaction information. This is the key to ensuring the accuracy of the traceability system, especially in the process of data input and transmission.

[0024] The data integrity assessment value is calculated based on the number of textile data transaction records, the number of actual textile data transactions, and the number of textile data transaction record verifications in order to verify whether the textile transaction data throughout its life cycle is fully recorded, and to determine whether the recorded transaction process data is complete and credible before tracing back to the source.

[0025] In the textile production process, the data generated in each link can be traced through blockchain technology. Blockchain can generate tamper-proof records for each production link, thereby ensuring the transparency and traceability of the product. In the blockchain, each "operation" between links can be regarded as a transaction, because it records events that occurred at a specific time and place, and these operations cannot be tampered with once they are on the chain. Through these transactions on the chain, the entire process of each textile from raw material procurement to the hands of the final consumer can be traced.

[0026] Specifically, each "operation" between links can be regarded as a "transaction". Here are some examples of key links: From raw material procurement to production: When the raw materials are purchased and ready to enter the production stage, the system will record a transaction to confirm that the batch of raw materials has arrived and passed the warehouse inspection. At this time, the transaction record from the raw materials procurement stage to the production stage is generated and written into the blockchain.

[0027] From production to quality inspection: Once the production process is completed (for example, a batch of fabrics is produced), the system will generate a transaction to mark the batch as completed and forwarded to quality inspection. This is a transaction between the production process and the quality inspection process. The quality inspection process will generate a new transaction by checking the product to confirm whether it meets the standards.

[0028] From quality inspection to logistics: When a product passes quality inspection, the data will generate a transaction, indicating that the product has passed the inspection and entered the logistics link. This means that the product is ready to be delivered to distributors or retailers, and the data handover between the quality inspection link and the logistics link is completed.

[0029] From logistics to sales: After the product is transported to the point of sale (such as a store or warehouse), the system will generate a transaction record, indicating that the product has been successfully transported and reached the sales link. This is a transaction between the logistics link and the sales link.

[0030] Sales to consumers: When a consumer purchases a textile, the system records a transaction, confirming that the product has been successfully sold. This is a transaction record from the sales link to the consumer.

[0031] The data generated in every link of textile production, whether it is the parameters in the production process or the logistics information, can be regarded as "transactions" in the blockchain. Each transaction represents the execution result of a link, and the blockchain technology ensures the data's immutability and full traceability.

[0032] The number of textile data transaction records refers to the total number of data transactions in all textile production, inspection, transportation and other links recorded in the blockchain, including the total number of successful transactions and failed transactions. For example, if there are two failures in the quality inspection process, there will be two failed transactions. Whenever a link (such as raw material procurement, production, quality inspection, transportation, etc.) is completed, the relevant transaction data will be written into the blockchain, and each record of these operations will increase the number of transactions.

[0033] For example, suppose a batch of textiles has gone through multiple links such as raw material procurement, production process, quality inspection, and transportation. Each link will generate a blockchain transaction record, and the sum of all these records is the "number of transaction records." If a product goes through 5 links, and each link generates a record, then the number of transaction records for this batch of products is 5, which is the number of transaction records in the blockchain.

[0034] The actual number of textile data transactions refers to the number of data transactions that actually occur, that is, the "number of transactions" generated under real circumstances, which may differ from the records on the blockchain.

[0035] The number of verifications of textile data transaction records refers to the number of times transaction records are verified in the blockchain network. In the blockchain, each transaction will go through the consensus verification process of the network nodes to confirm the validity and accuracy of the transaction. The number of verifications may be the same as the number of transaction records, but in some systems, the verification process may occur multiple times, especially in some complex production or logistics links.

[0036] For example, suppose a batch of textiles goes through multiple links, and each link generates a transaction record. These transaction records need to be verified by nodes in the blockchain to ensure their legitimacy. If each transaction in each link is verified by a node, then the number of verifications is the number of transaction records multiplied by the number of verification nodes. If there are 5 links to generate transactions, and each transaction is verified by 1 node, then the number of verifications is 5.

[0037] The data integrity assessment value is obtained as follows: ; In the formula, is the data integrity evaluation value, sj is the number of textile data transaction records, jl is the number of actual textile data transactions, and tz is the number of textile data transaction record verifications. When calculating, each formula in this embodiment can normalize the data as needed.

[0038] By verifying the number of transaction records, the authenticity and accuracy of transaction data can be ensured, thereby reducing data errors and improving overall data quality; by comparing the number of records with the actual number of transactions, potential missing or erroneous data can be identified and corrected; the actual number of transactions provides real transaction conditions, which helps analyze market demand and supply, supports more scientific data-driven decision-making, helps enhance the company's decision-making support capabilities, promotes the optimization of data management processes, and reduces risks.

[0039] Based on the complete textile data, the confidence of textile traceability access is analyzed to determine whether the textile traceability access behavior is credible.

[0040] The specific analysis process is: obtain the textile traceability access confidence data set, which includes the total number of textile traceability single access verifications, the total duration of textile traceability single access verifications, and the highest proportion of byte overlap between the IP address of the device used for textile traceability access and the IP addresses of each device stored in the database.

[0041] The total number of single-access verifications for textile traceability refers to the number of times the same textile traceability information is verified during a visit, which is recorded and counted by the Web server and application server; the total duration of a single-access verification for textile traceability refers to the total time consumed by a user from the start of a request to the final completion of the verification of the textile traceability information during a visit, which is recorded by the Web server and application monitoring system; the highest proportion of byte overlap between the IP address of the device used for textile traceability access and the IP addresses of each device stored in the database is used to measure the similarity between the device IP address used by the user when accessing and all device IP addresses stored in the database; the proportion of byte overlap indicates the degree of similarity between two IP addresses in certain bits. For example, if the two IPv4 addresses are 192.168.1.1 and 192.168.1.2, their first three bytes (192.168.1) overlap, and the user's IP address is obtained through network monitoring equipment and logging systems.

[0042] Based on the obtained textile traceability access confidence data set, a comprehensive analysis is performed to obtain the traceability access confidence value, which is used as the analysis basis for judging whether the textile traceability access behavior is credible; the traceability access confidence value is compared with the traceability access definition confidence value stored in the database; if the traceability access confidence value is higher than or equal to the traceability access definition confidence value, the textile traceability access behavior corresponding to the traceability access confidence value is credible; if the traceability access confidence value is lower than the traceability access definition confidence value, the textile traceability access behavior corresponding to the traceability access confidence value is untrustworthy, and a prompt is issued for the untrustworthy textile traceability access behavior.

[0043] If the user makes multiple visits during the verification process (for example, frequently requesting more information or viewing different textile data), the total duration will increase. A high number of verifications may also generally mean that the user has encountered some difficulties in obtaining the required information, thereby increasing the duration of their visit. This indicator is related to the user's identity tracking and access credibility. If a certain IP address has a high proportion of overlap with multiple storage address bytes in the database, it means that the user may be repeatedly using the same device to access, indicating the consistency of the access behavior. If the user behavior (total number and total duration) is abnormal (for example, frequent requests in a short period of time), and the IP address overlap in the blocking channel is high, the system may need to further review the user's behavior to ensure its security.

[0044] Based on identifying and handling untrustworthy traceability behaviors, enterprises can effectively manage market risks and reputation risks. By distinguishing between credible and untrustworthy behaviors, enterprises can rationally allocate resources and focus on credible access behaviors, thereby improving overall operational efficiency. By analyzing and judging the credibility of traceability access behaviors, enterprises can ensure that every link in the supply chain is transparent, thereby improving the reliability and efficiency of the supply chain. By counting and analyzing traceability confidence values, enterprises can make smarter business decisions based on data-driven methods, such as selecting suitable suppliers and identifying market opportunities.

[0045] The way to obtain the traceability access confidence value is as follows: ; In the formula, is the confidence value of traceability access, dn is the total number of single access verifications for textile traceability, dt is the total duration of single access verification for textile traceability, cb is the maximum proportion of bytes of overlap between the IP address of the device used for textile traceability access and the IP addresses of each device stored in the database, is the compensation factor of the set dn, is the compensation factor of the set dt, is the compensation factor of the set cb, and e is a natural constant.

[0046] Traceability access confidence value As the IP address of the device used for textile traceability access coincides with the bytes of each device IP address stored in the database, the image of the highest cb change is as follows Figure 3 As shown, analyzing the total number and duration of access verification can ensure that each traceability access is fully verified, thereby improving the accuracy of the data. By detecting the overlap rate of IP addresses, the credibility of the access source can be confirmed, false or duplicate access records can be avoided, and the consistency of records can be improved. Monitoring the IP addresses of different devices and their access patterns can more quickly identify and respond to potential abnormal or risky behaviors.

[0047] The compensation factors of dn, dt, and cb are obtained from the database. The total number of single-access verifications of textile traceability, the total duration of single-access verifications of textile traceability, the highest proportion of byte overlap between the IP address of the device used for textile traceability access and the IP address of each device stored in the database, and the mapping set of compensation factors of dn, dt, and cb are established based on historical data to obtain the compensation factors corresponding to the current dn, dt, and cb. Other compensation factors in this embodiment are obtained in the same way.

[0048] Based on the trusted textile traceability access behavior, the textile traceability query results are analyzed, and the textile traceability data are obtained by comparison. Based on the output traceability results (i.e. the compared textile traceability data), the traceability of the textile data is completed.

[0049] The specific analysis process is as follows: obtain the textile traceability query result data set, which includes the total number of bytes of the textile traceability query result, the total number of fields of the textile traceability query result, and the update frequency of the textile traceability query record. The total number of bytes refers to the total storage space occupied by a textile traceability query result, in bytes, including the total number of bytes of all data in the query result (including field name, field content, and other related information). The total number of bytes of the query result can be obtained through SQL query; the total number of fields refers to the total number of fields (or data columns) included in a specific traceability query result, which can be obtained through the metadata of the table (such as the table structure definition). SQL statements such as SHOW COLUMNS FROM table_name or DESCRIBE table_name usually return the number of fields in the table; the query record update frequency refers to the number of times the traceability query record is updated within a specific period of time. The update operation of the record can be monitored through the log table or the audit tracking function; Based on the obtained textile traceability query result data set, a comprehensive analysis is performed to obtain the traceability query result characteristic value, and the traceability query result characteristic value is used as the analysis basis for comparing the textile traceability data; the traceability query result characteristic value is compared with the textile traceability data corresponding to each traceability query result characteristic value stored in the database to obtain the textile traceability data corresponding to the traceability query result characteristic value.

[0050] The total number of bytes is usually proportional to the total number of fields. Each field takes up a certain amount of storage space, such as the storage of field name, field type and field value. The more fields there are, the more bytes there are. Provenance records with a larger total number of fields may contain more information. In theory, if the data structure is well designed, more fields can provide more specific context, which can be more specific when updating. If some information in these fields is updated frequently, the frequency of record updates will increase. A higher total number of bytes may mean more complex or diverse records, but this may also lead to decreased query and update performance, affecting the effectiveness and frequency of updates. The increased time and resources required to update large-scale data may lead to a decrease in update frequency.

[0051] The total number of bytes of textile traceability query results, the total number of fields of textile traceability query results, and the update frequency of textile traceability query records are characteristic parameters in textile data traceability, which help to confirm the credibility of the compared textile traceability data. By comparing the traceability query result characteristic values ​​with the textile traceability data corresponding to each traceability query result characteristic value stored in the database, and the query result characteristic values ​​with the data in the database, the textile traceability data corresponding to the traceability query result characteristic values ​​are obtained, which can confirm the specific traceability results of the data and reflect the real supply chain information. Automated data comparison can greatly reduce the time and labor of manual verification, making the query process faster and more efficient.

[0052] The method for obtaining the characteristic value of the traceability query result is as follows: ; In the formula, is the characteristic value of the traceability query result, zj is the total number of bytes of the textile traceability query result, zd is the total number of fields of the textile traceability query result, pl is the update frequency of the textile traceability query record, is the compensation factor of the set zj, is the compensation factor of the set zd, is the compensation factor of the set pl.

[0053] Tracing query result characteristic value As the total number of bytes zj of the textile traceability query results changes, the image is as follows Figure 4 As shown in the figure, by analyzing the total number of fields, it can ensure that the query results cover key information and improve the integrity and accuracy of the data. The analysis of the total number of fields and the total number of bytes can help the system provide more intuitive and rich query results. Combined with the analysis of the query record update frequency, the high-quality traceability query results obtained can provide accurate data support for management and help them refer to and analyze when making decisions.

[0054] Based on the textile traceability data obtained by comparison, the status of the textile traceability data is analyzed to obtain the traceability data status evaluation value. The traceability data status evaluation value is used to determine whether the output traceability result (i.e. the compared textile traceability data) has risk points that are recorded but incomplete or not updated in a timely manner. If there are risk points, although the output traceability result (i.e. the compared textile traceability data) is obtained, the output traceability result (i.e. the compared textile traceability data) is not completely reliable.

[0055] The specific analysis process is: obtain the textile traceability data status data set, which includes the proportion of missing product information in textile traceability data, the proportion of missing production information in textile traceability data, the proportion of missing supply chain information in textile traceability data, and the absolute value of the difference between the update frequency of textile traceability data and the reference update frequency. The missing ratio of product information in textile traceability data refers to the missing ratio of product information related to textiles (such as product name, model, ingredients, etc.). A high ratio means incomplete product information. It is obtained based on the database management system. The missing ratio of production information in textile traceability data reflects the missing ratio of information related to the production process (such as production date, manufacturer, production process, etc.). If this ratio is high, it means that there is insufficient transparency in the product production process. It is obtained based on the production management system. The missing ratio of supply chain information in textile traceability data involves the missing ratio of information in each link of the supply chain (such as supplier information, transportation information, inventory status, etc.). A high missing ratio may lead to poor supply chain management and increase uncertainty. It is obtained based on the supply chain management software. The absolute value of the difference between the update frequency of textile traceability data and the reference update frequency reflects the gap between the actual data update frequency and the preset reference update frequency. The larger the absolute value, the more likely it is that the data update lags behind expectations, which may affect the timeliness and real-time nature of the information. It is obtained based on the data management system. Based on the obtained textile traceability data status data set, a comprehensive analysis is conducted to obtain the traceability data status assessment value, which is used as the analysis basis for comparing the risk points of textile traceability data. The method for obtaining the traceability data status evaluation value is as follows: ; In the formula, is the traceability data status assessment value, cq is the proportion of missing product information in textile traceability data, sq is the proportion of missing production information in textile traceability data, gl is the proportion of missing supply chain information in textile traceability data, gx is the absolute value of the difference between the update frequency of textile traceability data and the reference update frequency, The update frequency of textile traceability data, Reference update frequency for textile traceability data, is the compensation factor of the set cq, is the compensation factor of the set sq, is the compensation factor of the set gl, is the compensation factor of the set gx.

[0056] Taking into account the missing ratio and update frequency of different types of information, the traceability data status assessment value can more comprehensively reflect the quality and reliability of textile traceability data, ensuring that the assessment covers all key aspects of the data set, thereby providing a more accurate risk assessment; by specifically analyzing the missing ratio of different information categories, enterprises can accurately locate the type and severity of data defects, which helps to prioritize the issues that have the greatest impact on traceability integrity and accuracy. Incorporating changes in data update frequency into the assessment can help companies promptly detect delays or anomalies in data updates, thereby quickly responding to maintain the real-time and accuracy of supply chain data, and further optimizing data management strategies.

[0057] The above-mentioned product information, production information and supply chain information are different parts that constitute a complete traceability system. If information in one dimension is lacking, other dimensions may also be affected. A high proportion of missing product information may affect the information records of production and supply chain. Low data update frequency often means outdated information, which may lead to an increase in the missing ratio. If the supply chain information is not updated in a timely manner, related production information and product information may also be missing due to lack of the latest data. The greater the absolute value of the difference between the update frequency and the reference update frequency, the more delayed the information update, which may directly affect the missing ratio. If the real-time update frequency of production information is much lower than the reference update frequency, it may cause the missing of relevant data, increasing the risks faced by the company.

[0058] By quantitatively analyzing the proportion of various types of missing information (such as product, production, and supply chain information), companies can clearly understand the integrity of the data and identify which aspects have a high missing rate, which can effectively improve data transparency and enable managers to quickly locate problems. The calculation of the traceability data status assessment value can help identify specific factors related to risks. If the proportion of missing production information of certain products is high, it may mean that the quality of the product cannot be effectively guaranteed, thus forming a risk point. The differences in various data missing situations and update frequencies can reveal potential risk areas. Evaluating the traceability data status assessment value can help companies establish a mechanism for continuous improvement. Companies can adjust internal processes and optimize data management strategies based on the evaluation results to improve the quality and efficiency of the textile traceability system. By comparing the changes in different evaluation cycles, quantitative effect analysis can be performed to verify the effectiveness of improvement measures. The analysis of the difference in the proportion of missing supply chain information and the update frequency in traceability data can directly help companies understand the vulnerable links in their supply chains and optimize supply chain management, thereby improving overall efficiency and reliability.

[0059] Based on the traceability data status assessment value, the risk points of textile traceability data are obtained by comparison.

[0060] The specific analysis process is: compare the traceability data status assessment value with the textile traceability data risk points corresponding to each traceability data status assessment value stored in the database to obtain the textile traceability data risk points corresponding to the traceability data status assessment value.

[0061] By updating and comparing the traceability data status assessment values ​​in real time, enterprises can continuously monitor the status of the entire supply chain, respond to any anomalies in a timely manner, ensure the stable operation of the supply chain, and systematically analyze risk points. Enterprises can optimize supply chain management strategies, improve efficiency and effectiveness. By systematically analyzing risk points, enterprises can optimize supply chain management strategies, improve efficiency and effectiveness. The early warning system helps enterprises take preventive measures to avoid or mitigate potential losses.

[0062] Example 2: Figure 2 As shown, a textile traceability system based on blockchain is used to implement the method in Example 1, including a textile data integrity judgment module, an access behavior confidence analysis module, a textile traceability data comparison module, a traceability data status evaluation value acquisition module and a textile traceability data risk point comparison module, wherein: The textile data integrity judgment module is used to analyze the integrity of textile data based on the blockchain network and determine whether the textile data is complete.

[0063] The access behavior confidence analysis module is used to analyze the confidence of textile traceability access based on complete textile data to determine whether the textile traceability access behavior is credible.

[0064] The textile traceability data comparison module is used to analyze the textile traceability query results based on the trusted textile traceability access behavior and compare the textile traceability data.

[0065] The traceability data status evaluation value acquisition module is used to analyze the textile traceability data status based on the textile traceability data obtained by comparison to obtain the traceability data status evaluation value.

[0066] The textile traceability data risk point comparison module is used to obtain the textile traceability data risk points based on the traceability data status assessment value.

Claims

1. A blockchain-based textile traceability method, characterized in that: The following steps are involved: Based on the blockchain network, the integrity of textile data is analyzed to determine whether the textile data is complete; Based on the complete textile data, the confidence of textile traceability access is analyzed to determine whether the textile traceability access behavior is credible; Based on the credible textile traceability access behavior, the textile traceability query results are analyzed and compared to obtain the textile traceability data; Based on the textile traceability data obtained by comparison, the textile traceability data status is analyzed to obtain the traceability data status evaluation value; Based on the traceability data status assessment value, the risk points of textile traceability data are obtained by comparison.

2. The blockchain-based textile traceability method according to claim 1, characterized in that: The process of analyzing the integrity of textile data and determining whether the textile data is complete is as follows: Acquire a textile data integrity data set, and obtain a data integrity assessment value based on a comprehensive analysis of the acquired textile data integrity data set, wherein the data integrity assessment value serves as an analysis basis for determining whether the textile data is complete; comparing the data integrity assessment value with a data integrity delimitation assessment value stored in a database; If the data integrity assessment value is equal to the data integrity delimitation assessment value, the textile data corresponding to the data integrity assessment value is complete; If the data integrity assessment value is not equal to the data integrity definition assessment value, the textile data corresponding to the data integrity assessment value is incomplete, and a prompt is issued for the incomplete textile data.

3. The blockchain-based textile traceability method according to claim 2 is characterized in that: The textile data integrity data set includes the number of textile data transaction records, the number of actual textile data transactions, and the number of textile data transaction record verifications.

4. The blockchain-based textile traceability method according to claim 1, characterized in that: The process of analyzing the confidence of textile traceability access and judging whether the textile traceability access behavior is credible is as follows: Obtain a textile traceability access confidence data set, which includes the total number of textile traceability single access verifications, the total duration of textile traceability single access verifications, and the highest percentage of overlap between the IP address of the device used for textile traceability access and the IP address bytes of each device stored in the database; Based on the obtained textile traceability access confidence data set, a comprehensive analysis is conducted to obtain the traceability access confidence value, which is used as the analysis basis for judging whether the textile traceability access behavior is credible. Comparing the traceability access confidence value with the traceability access bounded confidence value stored in the database; If the traceability access confidence value is higher than or equal to the traceability access definition confidence value, the textile traceability access behavior corresponding to the traceability access confidence value is credible; If the traceability access confidence value is lower than the traceability access definition confidence value, the textile traceability access behavior corresponding to the traceability access confidence value is untrustworthy, and a prompt is issued for the untrustworthy textile traceability access behavior.

5. The blockchain-based textile traceability method according to claim 4 is characterized in that: The traceability access confidence value is obtained in the following way: ； In the formula, is the confidence value of traceability access, dn is the total number of single access verifications for textile traceability, dt is the total duration of single access verification for textile traceability, cb is the maximum proportion of bytes of overlap between the IP address of the device used for textile traceability access and the IP addresses of each device stored in the database, is the compensation factor of the set dn, is the compensation factor of the set dt, is the compensation factor of the set cb, and e is a natural constant.

6. The blockchain-based textile traceability method according to claim 1, characterized in that: The process of analyzing the textile traceability query results and comparing the textile traceability data is as follows: Obtain a textile traceability query result data set, the textile traceability query result data set including the total number of textile traceability query result bytes, the total number of textile traceability query result fields, and the update frequency of textile traceability query records; Based on the obtained textile traceability query result data set, a comprehensive analysis is performed to obtain the traceability query result characteristic value, and the traceability query result characteristic value is used as the analysis basis for comparing the textile traceability data; The traceability query result characteristic value is compared with the textile traceability data corresponding to each traceability query result characteristic value stored in the database to obtain the textile traceability data corresponding to the traceability query result characteristic value.

7. The blockchain-based textile traceability method according to claim 6 is characterized in that: The method for obtaining the characteristic value of the traceability query result is: ； In the formula, is the characteristic value of the traceability query result, zj is the total number of bytes of the textile traceability query result, zd is the total number of fields of the textile traceability query result, pl is the update frequency of the textile traceability query record, is the compensation factor of the set zj, is the compensation factor of the set zd, is the compensation factor of the set pl.

8. The blockchain-based textile traceability method according to claim 1, characterized in that: The process of analyzing the textile traceability data status to obtain the traceability data status evaluation value is as follows: Obtain a textile traceability data status data set, which includes the proportion of missing product information in textile traceability data, the proportion of missing production information in textile traceability data, the proportion of missing supply chain information in textile traceability data, and the absolute value of the difference between the update frequency of textile traceability data and the reference update frequency; Based on the obtained textile traceability data status data set, a comprehensive analysis is performed to obtain the traceability data status assessment value, which is used as the analysis basis for comparing the risk points of textile traceability data; The method for obtaining the traceability data status evaluation value is as follows: ； In the formula, is the traceability data status assessment value, cq is the proportion of missing product information in textile traceability data, sq is the proportion of missing production information in textile traceability data, gl is the proportion of missing supply chain information in textile traceability data, gx is the absolute value of the difference between the update frequency of textile traceability data and the reference update frequency, The update frequency of textile traceability data, Reference update frequency for textile traceability data, is the compensation factor of the set cq, is the compensation factor of the set sq, is the compensation factor of the set gl, is the compensation factor of the set gx.

9. The blockchain-based textile traceability method according to claim 1, characterized in that: The process of obtaining the risk points of textile traceability data by comparing the traceability data status evaluation value is as follows: The traceability data status evaluation value is compared with the textile traceability data risk points corresponding to each traceability data status evaluation value stored in the database to obtain the textile traceability data risk points corresponding to the traceability data status evaluation value.

10. The blockchain-based textile traceability system is characterized by: The blockchain-based textile traceability method for implementing any one of claims 1 to 9 comprises a textile data integrity judgment module, an access behavior confidence analysis module, a textile traceability data comparison module, a traceability data status evaluation value acquisition module, and a textile traceability data risk point comparison module, wherein: The textile data integrity judgment module is used to analyze the integrity of textile data based on the blockchain network and determine whether the textile data is complete; The access behavior confidence analysis module is used to analyze the confidence of textile traceability access based on complete textile data to determine whether the textile traceability access behavior is credible; The textile traceability data comparison module is used to analyze the textile traceability query results based on the credible textile traceability access behavior and compare the textile traceability data; A traceability data status evaluation value acquisition module is used to analyze the textile traceability data status based on the textile traceability data obtained by comparison to obtain the traceability data status evaluation value; The textile traceability data risk point comparison module is used to obtain the textile traceability data risk points based on the traceability data status assessment value.

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